Version 1
: Received: 5 September 2022 / Approved: 7 September 2022 / Online: 7 September 2022 (10:20:49 CEST)
How to cite:
Kalita, D.; Lyakhov, P. Moving Object Detection Based on a Combination of Kalman Filter and Median Filtering. Preprints2022, 2022090109. https://doi.org/10.20944/preprints202209.0109.v1
Kalita, D.; Lyakhov, P. Moving Object Detection Based on a Combination of Kalman Filter and Median Filtering. Preprints 2022, 2022090109. https://doi.org/10.20944/preprints202209.0109.v1
Kalita, D.; Lyakhov, P. Moving Object Detection Based on a Combination of Kalman Filter and Median Filtering. Preprints2022, 2022090109. https://doi.org/10.20944/preprints202209.0109.v1
APA Style
Kalita, D., & Lyakhov, P. (2022). Moving Object Detection Based on a Combination of Kalman Filter and Median Filtering. Preprints. https://doi.org/10.20944/preprints202209.0109.v1
Chicago/Turabian Style
Kalita, D. and Pavel Lyakhov. 2022 "Moving Object Detection Based on a Combination of Kalman Filter and Median Filtering" Preprints. https://doi.org/10.20944/preprints202209.0109.v1
Abstract
The task of determining the distance from one object to another is one of the important tasks solved in robotics systems. Conventional algorithms rely on an iterative process of predicting distance estimates, which results in an increased computational burden. Algorithms used in robotic systems should require minimal time costs, as well as be resistant to the presence of noise. To solve these problems, the paper proposes an algorithm for Kalman combination filtering with a Goldschmidt divisor and a median filter. Software simulation showed an increase in the accuracy of predicting the estimate of the developed algorithm in comparison with the traditional filtering algorithm, as well as an increase in the speed of the algorithm. The results obtained can be effectively applied in various computer vision systems.
Keywords
Kalman filter; median filter; impulse noise; estimate prediction; object distance determination; lidar; value calibration; point cloud.
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.